Investigation of the Human Brain Metabolome to Identify Potential Markers for Early Diagnosis and Therapeutic Targets of Alzheimer’s Disease

• Published in: Analytical chemistry (Washington) Vol. 85; no. 3; pp. 1803 - 1811
• Main Authors: Graham, Stewart F, Chevallier, Olivier P, Roberts, Dominic, Hölscher, Christian, Elliott, Christopher T, Green, Brian D
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 05.02.2013
• Subjects: Aged; Aged, 80 and over; Alzheimer disease; Alzheimer Disease - diagnosis; Alzheimer Disease - drug therapy; Alzheimer Disease - metabolism; Alzheimer's disease; Biomarkers; Biomarkers - metabolism; Brain; Brain - metabolism; Brain - pathology; chemometrics; Cohort Studies; discriminant analysis; Drug Delivery Systems; Early Diagnosis; Female; gender; Humans; ions; least squares; Liquid chromatography; Male; Mass spectrometry; Medical diagnosis; Metabolism; metabolome; Metabolome - physiology; metabolomics; Middle Aged; pathophysiology; prediction
• ISSN: 0003-2700; 1520-6882; 1520-6882
• DOI: 10.1021/ac303163f

A study combining high resolution mass spectrometry (liquid chromatography-quadrupole time-of-flight-mass spectrometry, UPLC-QTof-MS) and chemometrics for the analysis of post-mortem brain tissue from subjects with Alzheimer’s disease (AD) (n = 15) and healthy age-matched controls (n = 15) was undertaken. The huge potential of this metabolomics approach for distinguishing AD cases is underlined by the correct prediction of disease status in 94–97% of cases. Predictive power was confirmed in a blind test set of 60 samples, reaching 100% diagnostic accuracy. The approach also indicated compounds significantly altered in concentration following the onset of human AD. Using orthogonal partial least-squares discriminant analysis (OPLS-DA), a multivariate model was created for both modes of acquisition explaining the maximum amount of variation between sample groups (Positive Mode-R2 = 97%; Q2 = 93%; root mean squared error of validation (RMSEV) = 13%; Negative Mode-R2 = 99%; Q2 = 92%; RMSEV = 15%). In brain extracts, 1264 and 1457 ions of interest were detected for the different modes of acquisition (positive and negative, respectively). Incorporation of gender into the model increased predictive accuracy and decreased RMSEV values. High resolution UPLC-QTof-MS has not previously been employed to biochemically profile post-mortem brain tissue, and the novel methods described and validated herein prove its potential for making new discoveries related to the etiology, pathophysiology, and treatment of degenerative brain disorders.